UID: REQ-002
Status: Active
Relations:
- File, file.py, hello_world
- File, file.rs, <file::FooTuple as file::FooTrait>::bar
Statement: The system shall show "Hello".
| private data class GummyFlavor( | |
| val name: String, | |
| val color: Color, | |
| ) | |
| private val gummyFlavors = listOf( | |
| GummyFlavor("Raspberry", Color(0xFFFF315F)), | |
| GummyFlavor("Tangerine", Color(0xFFFF8A24)), | |
| GummyFlavor("Grape", Color(0xFF9B5CFF)), | |
| ) |
If you want to setup the official GeforceNow app on Linux for gaming, use:
flatpak install -y --user flathub org.freedesktop.Sdk//24.08 org.freedesktop.Platform//24.08flatpak remote-add --user --if-not-exists GeForceNOW https://international.download.nvidia.com/GFNLinux/flatpak/geforcenow.flatpakrepoflatpak install -y --user GeForceNOW com.nvidia.geforcenowflatpak run com.nvidia.geforcenowflatpak override --user --nosocket=wayland com.nvidia.geforcenow, then launch again.Alternatively:
I'm using a small "Pico" or "Zero" dev board purchased from Amazon. It has a USB-C connector and numerous GPI pins broken out (0-15, 26-29).
Plug this board to the klipper host computer and use lsusb and you'll find this entry:
Bus 001 Device 005: ID 2e8a:0003 Raspberry Pi RP2 Boot
Seeing this will tell you that your computer is able to establish a usb connection to the dev board and you can now proceed with building and flashing Klipper.
Markdown-only workbook for Exam GH-600: Developing in Agentic AI Systems.
Last updated: May 24, 2026.
This version is organized around the official GH-600 domains. Each domain explains the concepts, shows the GitHub implementation artifacts, and includes examples you should be able to read in YAML, Markdown, CLI output, PR timelines, and audit logs.
Public sharing note: this guide is not an exam dump and does not contain real exam questions or answer choices. It is a structured study workbook built from official Microsoft and GitHub documentation, with practical examples written for learning and review.
| #!/usr/bin/env zsh | |
| # Multi-project worktree manager with Claude support | |
| # | |
| # ASSUMPTIONS & SETUP: | |
| # - Your git projects live in: ~/projects/ | |
| # - Worktrees will be created in: ~/projects/worktrees/<project>/<branch> | |
| # - New branches will be named: <your-username>/<feature-name> | |
| # | |
| # DIRECTORY STRUCTURE EXAMPLE: | |
| # ~/projects/ |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
| /* | |
| * MIT License | |
| * | |
| * Copyright (c) 2023 Fabio Lima | |
| * | |
| * Permission is hereby granted, free of charge, to any person obtaining a copy | |
| * of this software and associated documentation files (the "Software"), to deal | |
| * in the Software without restriction, including without limitation the rights | |
| * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| * copies of the Software, and to permit persons to whom the Software is |